CytometryML

CytometryML is an effort to produce a set of XML schemas to
define Cytometry data. This is an open effort, and we appreciate your
help.

Ideally, all of the groups and societies whose work is relevant
to Cytometry should join together to produce one standard. Unfortunately,
this may not be possible in the near future. However, these societies and
groups should, at least, try to maximize interoperability by using the same
data-types.

The data-types in CytometryML have been reused from the Digital
Imaging and Communications in Medicine (DICOM) standard, Flow Cytometry
Standard (FCS), and other standards. It has been possible, as shown in the
CytometryML schemas, to employ the same standard to describe Flow and Image
Cytometry. In fact, both a flow cytometer and a digital microscope were
derived by restriction from a generic cytometry instrument.

The DICOM hierarchy of patient, study, series, and instance has
served as the basis of the overall design of CytometryML. XML series and
instance schemas have been created and have been used to generate XML pages
for both flow cytometry and digital microscopy. These files have been
included in this release (Please see below.)

CytometryML XML Pages and Schemas

CytometryML consists of group of XML schemas, which are specific
to Cytometry and includes some general utility (library) schemas, which can
be used for other applications. The CytometryML schemas are available at no
cost. If you have any problems with or suggestions for these schemas, please
contact Robert C. Leif, email:rleif@rleif.com Please include the word
CytometryML in the Subject line.

Abstract of BiOS 2018 paper, CytometryML with DICOM and FCS

Abstract. Flow Cytometry Standard, FCS, and Digital Imaging and
Communications in Medicine standard, DICOM, are based on extensive, superb
domain knowledge, However, they are isolated systems, do not take advantage
of data structures, require special programs to read and write the data, lack
the capability to interoperate or work with other standards and FCS lacks
many of the datatypes necessary for clinical laboratory data. The large
overlap between imaging and flow cytometry provides strong evidence that both
modalities should be covered by the same standard.
Method: The XML Schema Definition Language, XSD 1.1 was used to translate FCS and/or DICOM objects.
A MIFlowCyt file was tested with published values.

Results Previously, a significant part of an XML standard based upon a
combination of FCS and DICOM has been implemented and validated with
MIFlowCyt data. Strongly typed translations of FCS keywords have been
constructed in XML. These keywords contain links to their DICOM and FCS
equivalents.

The attached PDF file of the PowerPoints for the CytometryML with DICOM and FCS(Keywords)
presentation at SPIE BiOS 2018
PowerPoints

The attached ZIP file of CytometryML schemas and associated files of Keywords at
SPIE BiOS 2018 Schemas and other
files

June 12, 2017 Release of CytometryML

Abstract

Introduction: Although FCS is based on extensive, superb domain knowledge,
it is an isolated system, does not take advantage of data structures and
requires special programs to read and write the data. The large overlap
between imaging and flow cytometry provides strong evidence that both
modalities should be covered by the same standard. However diagnostic imaging
to a large extent is described by DICOM, and eventually Health Level 7.

Problem: One major change in nomenclature of FCS 4.0 is that the term
parameter has been changed to dimension. However, to maintain backward
compatibility the letter “P” has been retained as the second character in FCS
Keywords.

Solution: A study has been started to determine the amount of effort
to translate FCS Keywords into XML elements. The XML schemas were translated
into XML pages and are being tested with the values in Blimkie_et_al.
Individual CytometryML elements have been linked to a FCS Keyword and/or
DICOM by the inclusion of an attribute.

The feasibility of translating FCS and DICOM into XML has been established.
XML attributes can be used as pointers to both FCS and DICOM datatypes. The
translation of FCS Keywords and DICOM into XML Schema Definition Language,
XSD 1.1 datatypes has become routine. The use of XSD elements instead of
attributes improves the efficiency and clarity of the translation of
datatypes into XSD and subsequently XML.

The attached zip file CytometryML12Jun17 contains 87
schema files; the total size of the code is about 1.3 megabytes. It also
contains a PDF of a poster presented at Cyto 2017

August 1, 2016 Release of CytometryML

Readme

This file contains approximatey 87 schema files and its total
size is about 1.3 megabytes. One of the major purposes of these schemas is to
describe the content of a MIFlowCyt file or section of a document. Another is
to provide a description of a Cytometry experiment that is sufficient for the
experiment to be repeated.

Although these schemas are still a work in progress, they may
be of use as the basis of a design or reuse of the actual code. Error
reporting, suggestions, collaboration, etc. would be greatly appreciated.

Analysis of the data is the subject of the present ACS software
development. For instance, the CytometryML quality schema needs assistance to
be extended into statistics that are relevant to Cytometry.

I have followed the principle of standards paucity by
translating many DICOM entities into XML. DICOM attributes translate into XML
elements. In the case of both FCS and DICOM, one or more XML attributes are
been used to provide a link back to the original standard. For DICOM, the
attributes correspond to the Tag and Value Representation (VR). From the
DICOM DATA DICTIONARY (Vol 6), "Tag: A unique identifier for an element of
information. Tags are composed of an ordered pair of numbers (a Group Number
followed by an Element Number). “The Tag is the unique value that identifies
every DICOM data element”

The Value Representation (VR) is the datatype or class of a
DICOM element. There are only 28 VRs. Each of which is expressed as a
combination of two letters. The DICOM Value Multiplicity (VM) corresponds to
the permitted occurrence of an XML element. The FCS Keywords are also
reproduced as XML attributes.

One significant difference between the ISAC ACS and CytometryML
is that CytometryML follows the model of splitting measurements up into a
series and an instance. CytometryML defines the series as those steps to the
specimen prior to the specific staining of each sample and the instance is
the operations on the individual samples including their specific staining
and or other preparatory steps.

The description of the instrument is split into two parts. The
first is the Series, which includes all of those actions or items that are
common to the performance of the processing and measuring of the samples.

The second is the instance, which includes all of those items
that are unique to processing of the individual aliquots of cells or
particles. These collection of items that are split into individual Instances
include items that have different settings, such as the optical, electronics
and/ or software that are specific for the individual measurements.

ISAC_5Jun16_Poster_Code.zip
This file contains, as of 5 June, 2016, the Fluorophore table schema
(cas.xsd) and associated schemas and a web page (cas_List21may16.xml) based
upon the cas.xsd schema Validating these schemas requires the use of an
XSD1.1 parser. Validating the web page includes the capability to process
NVDL (Namespace-based Validation Dispatching Language. Presently
cas_List21may16.xml produces the table correctly with Microsoft Edge and the
version of Windows Explorer that comes with Edge. The Cytometry Metadata In
XML And Xhtml5 Cyto 2016 poster, which is also available from this web page,
contains these results.

Obsolete! These schemas describe a Fluorochrome Table that
contains a Chemical Abstract service (CAS) Number Fluorochrome Table as of February 5, 2016
This ZIP file contains the CAS schemas, which demonstrate the feasibility of
using a cascading style sheet with elements from xhtml5 to create a table
consisting of XML elements. Validating these schemas requires the use of an
XSD1.1 parser that includes the capability to process NVDL (Namespace-based
Validation Dispatching Language).

miflowcyt5feb16.zip This file
contains, as of February 5, 2016, the MIFlowCyt schemas and a web page based
upon the Experiment Overview element (instance1.xml). Validating these
schemas requires the use of an XSD1.1 parser that includes the capability to
process NVDL (Namespace-based Validation Dispatching Language).

MIFlowCyt.ZIP This file contains the
MIFlowCyt schemas and the Experiment Overview web page, as of October 26,
2014. Validating these schemas requires the use of an XSD1.1 parser.

Zip file of CytometryML and web pages ,
as of 26 January, 2013. This ZIP contains most of the CytometryML schemas
including all that are involved on relations and an XML example,
Relation_Image_List21Jan13.xsd. Validating these schema requires the use of
an XSD1.1 parser, such as Xerxes in oXygen 14.1.

CytometryML Data List with Relationships, ISAC CYTO 2012
(B84 190)

Robert C. Leif*a and Stephanie H.
Leifa

aXML_Med, a Division of Newport Instruments,

3345 Hopi Place, San Diego, CA 92117

CytometryML is an XML schema based translation, extension, and
amalgamation of the DICOM and ISAC standards. CytometryML consists of 5 major
XML schemas: Relations, Series, Instance, Instrument, and Specimen; it also
includes Image, and List-Mode schemas. Series metadata, which is specific for
an entire collection of images and/or list-mode files produced by a single
instrument and derived from a single specimen, is stored together with
related metadata files in an EPUB container (ZIP) file. Each Instance
container file includes binary image and/or list-mode files together with
related metadata files that are specific for a single or closely related
group of instrument runs from a single specimen. The ISAC Archival Cytometry
Standard (ACS) proposed Table of Contents schema including its Resource
Description Framework (RDF) capabilities has been extended, modified, and
renamed for use in the Instance schema. The replacement of standard RDF
syntax by a simple sentence (element) based format (Subject, Predicate, and
Object) permits multiple relations between two file references that can be in
both directions. Extended ISAC
CYTO 2012 Poster

Recent Publications

SPIE BiOS 2016 Preprint.

Introduction: The International Society for Advancement of
Cytometry (ISAC) has created a standard for the Minimum Information about a
Flow Cytometry Experiment (MIFlowCyt 1.0). CytometryML will serve as a common
metadata standard for flow and image cytometry (digital microscopy). Methods:
The MIFlowCyt data-types were created, as is the rest of CytometryML, in the
XML Schema Definition Language (XSD1.1). The datatypes are primarily based on
the Flow Cytometry and the Digital Imaging and Communication (DICOM)
standards. A small section of the code was formatted with standard HTML
formatting elements (p, h1, h2, etc.). Results:1) The part of MIFlowCyt that
describes the Experimental Overview including the specimen and substantial
parts of several other major elements has been implemented as CytometryML XML
schemas (www.cytometryml.org). 2) The feasibility of using MIFlowCyt to
provide the combination of an overview, table of contents, and/or an index of
a scientific paper or a report has been demonstrated. Previously, a sample
electronic publication, EPUB, was created that could contain both MIFlowCyt
metadata as well as the binary data. Conclusions: The use of CytometryML
technology together with XHTML5 and CSS permits the metadata to be directly
formatted and together with the binary data to be stored in an EPUB
container. This will facilitate: formatting, data- mining, presentation, data
verification, and inclusion in structured research, clinical, and regulatory
documents, as well as demonstrate a publication’s adherence to the MIFlowCyt
standard, promote interoperability and should also result in the textual and
numeric data being published using web technology without any change in
composition.

Introduction: The development of cytometry standards is
complicated by their being relevant to pathology and biological science,
which already have standards. CytometryML, the cytometry markup language, is
an XML standard for flow and image cytometry, which includes both objects and
their relationships, and is based upon existing standards: the International
Society for Advancement of Cytometry ( ISAC) FCS, Digital Imaging and
Communication in Medicine ( DICOM), and International Digital Publishing
Forum (EPUB).

Methods: The CytometryML schemas are written in XML Schema
Definition (XSD1.1). Object-oriented methodology was employed to create the
CytometryML schemas, which were tested by translating specific XSD elements
into XML and filling in the values. The attribute based syntax description of
relationships in the Resource Description Framework (RDF) has been replaced
by an XSD element based implementation. The ISAC Archival Cytometry Standard
(ACS) concept of a zipped data container file was further refined to be a
EPUB file. Since Table of Contents information is present in an EPUB
container, it was minimized in the Relations schema, which replaced the ToC
schema of the ACS and includes a modified and extended version of the ToC RDF
capabilities.

Results: An XML based system that includes the DICOM specified
separation of series and instances and includes relationships has been
created.

Conclusions: CytometryML and EPUB could be used for the
transmission of research and medical data and be extension some of the
pathology part of DICOM. The CytometryML version of RDF in XSD could be
extended to provide XSD with full RDF capabilities.

CytometryML is an XML schema based translation, extension and
amalgamation of the DICOM and ISAC standards. CytometryML consists of 4 major
XML schemas: Series, Instance, Instrument, and Specimen; it also includes
Image and List-Mode schemas. Series metadata, which is specific for an entire
collection of images and/or list-mode files produced by a single instrument
and derived from a single specimen, is stored together with associated
metadata files in a container (ZIP) file. Each Instance container file
includes binary image and/or list-mode files together with associated
metadata files that are specific for a single or closely related group of
instrument runs from a single specimen. The Archival Cytometry Standard (ACS)
proposed Table of Contents schema including its Resource Description
Framework (RDF) capabilities has been extended and modified for use in the
Instance schema.

The integration of cytomics research and healthcare
informatics will facilitate technology transfer and reduce medical costs.
The CytometryML prototype of the Advanced Cytometry Standard (ACS) has the
benefits of including microscopic image and flow list-mode data, being
based on XML and thus is compatible with existing medical and scientific
informatics standards, such as HL7, and employing a design based upon the
Digital Imaging and Communications in Medicine (DICOM) standard. The reuse
of the well tested DICOM model resulted in a great decrease in the design
and documentation effort and increased probability of reliability. Schemas
for flow cytometers and microscopes have been created. XML schemas for two
related types of container (ZIP) files have been specified for a set of
measurements. The series and instance containers respectively include the
metadata that is constant and the metadata that is specific to an
individual or small closely related group of measurements. (� 2009
WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

The lack of software interoperability with respect to gating
due to lack of a standardized mechanism for data exchange has traditionally
been a bottleneck, preventing reproducibility of flow cytometry (FCM) data
analysis and the usage of multiple analytical tools. To facilitate
interoperability among FCM data analysis tools, members of the
International Society for the Advancement of Cytometry (ISAC) Data
Standards Task Force (DSTF) have developed an XML-based mechanism to
formally describe gates (Gating-ML). Gating-ML, an open specification for
encoding gating, data transformations and compensation, has been adopted by
the ISAC DSTF as a Candidate Recommendation. Gating-ML can facilitate
exchange of gating descriptions the same way that FCS facilitated for
exchange of raw FCM data. Its adoption will open new collaborative
opportunities as well as possibilities for advanced analyses and methods
development. The ISAC DSTF is satisfied that the standard addresses the
requirements for a gating exchange standard. � 2008 International Society
for Advancement of Cytometry

A fundamental tenet of scientific research is that published
results are open to independent validation and refutation. Minimum data
standards aid data providers, users, and publishers by providing a
specification of what is required to unambiguously interpret experimental
findings. Here, we present the Minimum Information about a Flow Cytometry
Experiment (MIFlowCyt) standard, stating the minimum information required
to report flow cytometry (FCM) experiments. We brought together a
cross-disciplinary international collaborative group of bioinformaticians,
computational statisticians, software developers, instrument manufacturers,
and clinical and basic research scientists to develop the standard. The
standard was subsequently vetted by the International Society for
Advancement of Cytometry (ISAC) Data Standards Task Force, Standards
Committee, membership, and Council. The MIFlowCyt standard includes
recommendations about descriptions of the specimens and reagents included
in the FCM experiment, the configuration of the instrument used to perform
the assays, and the data processing approaches used to interpret the
primary output data. MIFlowCyt has been adopted as a standard by ISAC,
representing the FCM scientific community including scientists as well as
software and hardware manufacturers. Adoption of MIFlowCyt by the
scientific and publishing communities will facilitate third-party
understanding and reuse of FCM data. � 2008 International Society for
Advancement of Cytometry

Background: Flow Cytometry Standard (FCS) was initially
created to standardize software researchers use to analyze, transmit, and
store data produced by flow cytometers and sorters. Because of the clinical
utility of flow cytometry, it is necessary to have a standard consistent
with the requirements of medical regulatory agencies.

Method:1) Extend the existing mapping of FCS to the
Digital Imaging and Communications in Medicine (DICOM) standard to include
list-mode data produced by flow, laser scanning cytometry, and microscopic
image cytometry. FCS list-mode was mapped to the DICOM Waveform Information
Object. 2) Create a collection of XML schemas to express the DICOM
analytical cytology text based data-types except for large binary objects.
3) Accomplish this creation of a cytometry markup language, CytometryML, in
an open environment that is subject to continuous peer review.

Results:The feasibility of expressing the data
contained in FCS, including list-mode in DICOM, has been demonstrated; and
a preliminary mapping for list-mode data in the form of XML Schemas and
documents has been completed. DICOM permits the creation of indices that
can be used to rapidly locate in a list-mode file the cells that are
members of a subset. DICOM and its coding schemes for other medical
standards can be represented by XML schemas, which can be combined with
other relevant XML applications, such as Mathematical Markup Language
(MathML).

Conclusions:The use of XML format based on DICOM for
analytical cytology has met most of the previously specified requirements
and appears capable of meeting the others; therefore, the present FCS
should be retired and replaced by an open, XML based standard,
CytometryML.

Abstracts of Other Papers Concerning Cytometry Standards
That Can Be Downloaded:

The goal of this work is to develop a safe software
construction means for an XML based data standard for a class of medical
devices, cytometry instruments. Unfortunately, the amount of empirical
evidence to archive this goal is minimal. Therefore, technologies
associated with high reliability were employed together with reuse of
existing designs.

The basis for a major part of the design was the Digital
Imaging and Communications in Medicine (DICOM) standard and the Flow
Cytometry Standard (FCS). Since the DICOM Standard is a Class II device,
the safety of software should be maximized. The XML Schema Definition
Language (XSDL) has been used to develop schemas that maximize readability,
modularity, strong typing, and reuse. An instance and an instrument XML
schema were created for data obtained with a microscope by importing
multiple schemas that each consisted of a class that described one object.
This design was checked by validating the schemas and creating XML pages
from them.

Introduction: The International Society for
the Advancement of Cytometry (ISAC) Data Standards Task Force (DSTF) is
developing a new Advanced Cytometry Specification (ACS). DICOM has
developed and is extending a pathology extension. The work of both groups
is complementary with some overlap. Interoperation would benefit both
groups and permit each to benefit from the other�s expertise.

Methods: The design and implementation of the
CytometryML version of the ACS schemas have been based on each schema
describing one object (modularity), iterative (spiral) development,
inheritance, and reuse of data-types and their definitions from DICOM, Flow
Cytometry Standard, and other standards.

Results: These schemas have been validated
with two tools and XML pages were generated from highest level schemas.
Binary image data and its associated metadata are stored together in a zip
file based container. A schema for a table of contents, which is one of the
metadata files of this container, has recently been developed and reported
upon. The binary image data is placed in one file in the container; and the
metadata associated with an image in another. The schema for the image
metadata file includes elements that are based on the DICOM design. This
image schema includes descriptions of the acquisition context, image
(including information on compression), specimen, slide, transmission
medium, major optical parts, optical elements in one or more optical
channels, detectors, and pixel format. The image schema describes both
conventional camera systems and scanning or confocal systems.

Introduction: The highest priority for the
Advanced Cytometry Standard (ACS) is the interpretation of list-mode
cytometry measurements. Other priorities of lesser importance are the
capacity to reproduce a cytometry measurement and the implementation of a
digital microscopy image standard. The sequential nature of these
requirements is being accommodated by a flexible, modular design. A major
feature of this modular design is the creation of a design for an Advanced
Cytometry Standard Container (ACSC) that includes a Table of Contents (ToC)
XML file, one or more binary data containing files and files that contain
the meta-data that describes the binary data.

Methods: The design and partial
implementation of the CytometryML schemas have been based on the techniques
of modularity (each schema describing one object), iterative (spiral)
development, inheritance, and reuse. Data-types including their definitions
have been reused from DICOM, FCS, and other standards.

Results: A prototype ToC schema together with
prototypes of many of the schemas that describe the contents of the ACSC
have been created together with their supporting schemas. These schemas
have been validated with two tools and XML pages were generated from the
main element(s) of the highest level schemas. These elements describe the
table of contents of the zipped container file and a flow-cytometry
instrument. The zipped container file (ACSC) describes and contains the
meta and binary data.

Introduction: The International Society for Analytical
Cytology, ISAC, is developing a new combined flow and image Analytical
Cytometry Standard (ACS). This standard needs to serve both the research
and clinical communities. The clinical medicine and clinical research
communities have a need to exchange information with hospital and other
clinical information systems.

Results: CytometryML has been created and serves as a
prototype and source of experience for the following: the Analytical
Cytometry Standard (ACS) 1.0, the ACS container, Minimum Information about
a Flow Cytometry Experiment (MIFlowCyt), and Requirements for a Data File
Standard Format to Describe Flow Cytometry and Related Analytical Cytology
Data. These requirements provide a means to judge the appropriateness of
design elements and to develop tests for the final ACS. The requirements
include providing the information required for understanding and
reproducing a cytometry experiment or clinical measurement, and for a
single standard for both flow and digital microscopic cytometry. Schemas
proposed by other members of the ISAC Data Standards Task Force (e.g,
Gating-ML) have been independently validated and have been integrated with
CytometryML. The use of netCDF as an element of the ACS container has been
proposed by others and a suggested method of its use is proposed.

Because of the differences in the requirements, needs, and
past histories including existing standards of the creating organizations,
a single encompassing cytology-pathology standard will not, in the near
future, replace the multiple existing or under development standards.
Except for DICOM and FCS, these standardization efforts are all based on
XML. CytometryML is a collection of XML schemas, which are based on the
Digital Imaging and Communications in Medicine (DICOM) and Flow Cytometry
Standard (FCS) datatypes. The CytometryML schemas contain attributes that
link them to the DICOM standard and FCS. Interoperability with DICOM has
been facilitated by, wherever reasonable, limiting the difference between
CytometryML and the previous standards to syntax. In order to permit the
Resource Description Framework, RDF, to reference the CytometryML
datatypes, id attributes have been added to many CytometryML elements. The
Laboratory Digital Imaging Project (LDIP) Data Exchange Specification and
the Flowcyt standards development effort employ RDF syntax. Documentation
from DICOM has been reused in CytometryML. The unity of analytical cytology
was demonstrated by deriving a microscope type and a flow cytometer type
from a generic cytometry instrument type. The feasibility of incorporating
the Flowcyt gating schemas into CytometryML has been demonstrated.
CytometryML is being extended to include many of the new DICOM Working
Group 26 datatypes, which describe patients, specimens, and analytes. In
situations where multiple standards are being created, interoperability can
be facilitated by employing datatypes based on a common set of semantics
and building in links to standards that employ different syntax.

Cytology automation and research will be enhanced by the
creation of a common data format. This data format would provide the
pathology and research communities with a uniform way for annotating and
exchanging images, flow cytometry, and associated data. This specification
and/or standard will include descriptions of the acquisition device,
staining, the binary representations of the image and list-mode data, the
measurements derived from the image and/or the list-mode data, and
descriptors for clinical/pathology and research. An international,
vendor-supported, non-proprietary specification will allow pathologists,
researchers, and companies to develop and use image capture/analysis
software, as well as list-mode analysis software, without worrying about
incompatibilities between proprietary vendor formats.

Presently, efforts to create specifications and/or
descriptions of these formats include the Laboratory Digital Imaging
Project (LDIP) Data Exchange Specification; extensions to the Digital
Imaging and Communications in Medicine (DICOM); Open Microscopy Environment
(OME); Flowcyt, an extension to the present Flow Cytometry Standard (FCS);
and CytometryML.

The feasibility of creating a common data specification for
digital microscopy and flow cytometry in a manner consistent with its use
for medical devices and interoperability with both hospital information and
picture archiving systems has been demonstrated by the creation of the
CytometryML schemas. The feasibility of creating a software system for
digital microscopy has been demonstrated by the OME. CytometryML consists
of schemas that describe instruments and their measurements. These
instruments include digital microscopes and flow cytometers. Optical
components including the instruments� excitation and emission parts are
described. The description of the measurements made by these instruments
includes the tagged molecule, data acquisition subsystem, and the format of
the list-mode and/or image data. Many of the CytometryML data-types are
based on the Digital Imaging and Communications in Medicine (DICOM). Binary
files for images and list-mode data have been created and read.

CytometryML is a proposed new Analytical Cytology (Cytomics)
data standard, which is based on a common set of XML schemas for encoding
flow cytometry and digital microscopy text based data types (metadata).
CytometryML schemas reference both DICOM (Digital Imaging and
Communications in Medicine) codes and FCS keywords. Flow Cytometry Standard
(FCS) list-mode has been mapped to the DICOM Waveform Information Object.
The separation of the large binary data objects (list mode and image data)
from the XML description of the metadata permits the metadata to be
directly displayed, analyzed, and reported with standard commercial
software packages; the direct use of XML languages; and direct interfacing
with clinical information systems. The separation of the binary data into
its own files simplifies parsing because all extraneous header data has
been eliminated. The storage of images as two-dimensional arrays without
any extraneous data, such as in the Adobe� Photoshop� RAW format,
facilitates the development by scientists of their own analysis and
visualization software. Adobe Photoshop provided the display infrastructure
and the translation facility to interconvert between the image data from
commercial formats and RAW format. Similarly, the storage and parsing of
list mode binary data type with a group of parameters that are specified at
compilation time is straight forward. However when the user is permitted at
run-time to select a subset of the parameters and/or specify results of
mathematical manipulations, the development of special software was
required. The use of CytometryML will permit investigators to be able to
create their own interoperable data analysis software and to employ
commercially available software to disseminate their data.

Cytometry Markup Language, CytometryML, is a proposed new
analytical cytology data standard. CytometryML is a set of XML schemas for
encoding both flow cytometry and digital microscopy text based data types.
CytometryML schemas reference both DICOM (Digital Imaging and
Communications in Medicine) codes and FCS keywords. These schemas provide
representations for the keywords in FCS 3.0 and will soon include DICOM
microscopic image data. Flow Cytometry Standard (FCS) list-mode has been
mapped to the DICOM Waveform Information Object. A preliminary version of a
list mode binary data type, which does not presently exist in DICOM, has
been designed. This binary type is required to enhance the storage and
transmission of flow cytometry and digital microscopy data. Index files
based on Waveform indices will be used to rapidly locate the cells present
in individual subsets. DICOM has the advantage of employing standard file
types, TIF and JPEG, for Digital Microscopy.

Using an XML schema based representation means that standard
commercial software packages such as Excel and MathCad can be used to
analyze, display, and store analytical cytometry data. Furthermore, by
providing one standard for both DICOM data and analytical cytology data, it
eliminates the need to create and maintain special purpose interfaces for
analytical cytology data thereby integrating the data into the larger DICOM
and other clinical communities. A draft version of CytometryML is available
at www.newportinstruments.com.

Flow Cytometry data can be directly mapped to the Digital
Imaging and Communications in Medicine, DICOM standard. A preliminary
mapping of list-mode data to the DICOM Waveform information Object will be
presented. This mapping encompasses both flow and image list-mode data.
Since list-mode data is also produced by digital slide microscopy, which
has already been standardized under DICOM, both branches of Analytical
Cytology can be united under the DICOM standard. This will result in the
functionality of the present International Society for Analytical Cytology
Flow Cytometry Standard, FCS, being significantly extended and the
elimination of the previously reported FCS design deficiencies. Thus, The
present Flow Cytometry Standard can and should be replaced by a Digital
Imaging and Communications in Medicine, DICOM, standard. Expression of
Analytical Cytology data in any other format, such as XML, can be made
interoperable with DICOM by employing the DICOM data types. A fragment of
an XML Schema has been created, which demonstrates the feasibility of
expressing DICOM data types in XML syntax. The extension of DICOM to
include Flow Cytometry will have the benefits of 1) retiring the present
FCS, 2) providing a standard that is ubiquitous, internationally accepted,
and backed by the medical profession,and 3) interoperating with the
existing medical informatics infrastructure.

The addition of a list mode data type to the Digital Imaging
and Communications in Medicine standard, DICOM will enhance the storage and
transmission of digital microscopy data and extend DICOM to include flow
cytometry data. This would permit the present International Society for
Analytical Cytology Flow Cytometry Standard to be retired. DICOM includes:
image graphics objects, specifications for describing: studies, reports,
the acquisition of the data, work list management, and the individuals
involved (physician, patient, etc.). The glossary of terms (objects)
suitable for use with DICOM has been extended to include the collaborative
effort of Logical Observation Identifier Names and Codes (LOINC) and
Systematized Nomenclature of Human and Veterinary Medicine (SNOMED) to
create a consistent, unambiguous clinical reference terminology. It also
appears that DICOM will be a significant part of the Common Object Request
Broker Architecture, CORBA.

The International Society for Analytical Cytology, ISAC,has
developed a Flow Cytometry Standard (FCS) to permit data interchange. ISAC
will soon replace Flow Cytometry Standard 2.0 (FCS2.0) with FCS3.0.
Unfortunately,the proposed FCS3.0 is still fraught with problems, which are
of sufficient magnitude as to warrant its early replacement. The most
reasonable replacement is as a supplement to the Digital Imaging and
Communications in Medicine, DICOM 3.0, standard. The recent digital
microscopy extension of DICOM can be extended and modified to include flow
cytometry data. DICOM includes: image graphics objects, specifications for
describing: studies, reports,the acquisition of the data and the
individuals involved, physician, patient, etc. Storing the present FCS data
in a database, which has already been accomplished with the QC Tracker
software, will facilitate the transition of FCS to DICOM.